A Passenger Flow Analysis Method Through Ride Behaviors on Massive Smart Card Data

2018 ◽  
pp. 374-382
Author(s):  
Weilong Ding ◽  
Zhuofeng Zhao ◽  
Han Li ◽  
Yaqi Cao ◽  
Yang Xu
Keyword(s):  
Smart Card ◽  
Flow Analysis ◽  
Analysis Method ◽  
Passenger Flow ◽  
Smart Card Data

Recognizing the Critical Stations in Urban Rail Networks: An Analysis Method Based on the Smart-Card Data

2019 ◽  
Vol 11 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Xin Yang ◽  
Haodong Yin ◽  
Jianjun Wu ◽  
Yunchao Qu ◽  
Ziyou Gao ◽  
...  
Keyword(s):  
Smart Card ◽  
Analysis Method ◽  
Smart Card Data ◽  
Urban Rail

Analysis of subway passenger flow based on smart card data

2020 ◽  
Author(s):  
Yi Wang ◽  
Weilin Zhang ◽  
Fan Zhang ◽  
Ling Yin ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Smart Card ◽  
Passenger Flow ◽  
Smart Card Data

scholarly journals Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoqing Dai ◽  
Lijun Sun ◽  
Yanyan Xu
Keyword(s):  
Model Selection ◽  
Smart Card ◽  
Probabilistic Model ◽  
Travel Demand ◽  
Combination Method ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Smart Card Data

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

scholarly journals Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

2021 ◽  
Author(s):  
Christian Martin Mützel ◽  
Joachim Scheiner
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Mobility Patterns ◽  
Transit Planning ◽  
Transit Systems ◽  
Passenger Flow ◽  
Smart Card Data ◽  
Depth Analysis ◽  
Spatio Temporal ◽  
The Impact

AbstractModern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

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